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What is the purpose of cross-validation in machine learning?
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Cross-validation is a crucial technique in machine learning used to assess how well a predictive model will generalize to an independent dataset. The primary purpose is to estimate the performance of a model on unseen data, helping to avoid overfitting and providing insights into the model's reliability.
Key Purposes:
- Model Evaluation: Cross-validation provides a more reliable estimate of a model's performance than simply training and testing on a single train/test split.
- Hyperparameter Tuning: It helps in selecting the best hyperparameters for a model by evaluating performance across multiple validation sets.
- Model Selection: It facilitates comparing different models to choose the one that is most likely to perform well on new, unseen data.
- Overfitting Detection: By evaluating performance on multiple validation sets, cross-validation helps detect if a model is overfitting the training data.
In essence, cross-validation provides a robust method to ensure the model is not just memorizing the training data but is actually learning to generalize, leading to better predictions in real-world scenarios.
More information can be found here: scikit-learn.org